Connectome assembled with three-dimensional images of single neurons obtained via three-dimensional imaging of neuro-tissue is regarded as an important step to understand how neuro-system work and has started in several model animals like fruit fly and mouse. To handle the big data of neuroimages, segmenting single-neuron morphology from raw neuroimages without losing fibers or adding noise and then tracing them for 3D reconstruction represents a key challenge.
Manual segmentation can correctly extract the single neuron images from the raw data, but it is labor-intensive and time-consuming. Thus algorithms for automatic segmentation for neuroimages were developed. A traditional segmentation method is to take an intermediate signal intensity value as a threshold directly, thereby, filter out those voxels whose signal intensity being lower than the intensity threshold directly. The traditional methods treat the importance of a voxel intuitively equivalent to its own image signal intensity or the local intensity distribution around the voxel. However, neuroimages usually do not have uniform quality because the imaging condition thereof is not identical. Therefore, there may be some voxels which are important in structure (for example the voxels which are located at the upstream branches, or at the major branch points) but have weak intensity of signal. Such voxels could be deleted in the traditional denoising and segmenting methods, which result in that the whole tree of the downstream branches of the deleted voxels disappears. Thus, the obtained tree-shaped neuron structure is incorrect. Another traditional method captures images of a sample many times, collects a number of images of different intensities of signal and combines them into a high-dynamic-range image to stabilize signal. However, such method will significantly increase the time spent for capturing the images, and damage biological tissues for imaging.
Accordingly, there is still a need for a solution which can solve the problem of the tradition technique for automatic segmentation of neuroimages.